Thanks, I'll take a look at Rprof... but I think what i'm missing is
facility with R idiom to get around the looping, and no amount of
profiling will help me with that :)
also, full working code is provided in my original post (see toward the
bottom).
on 06/05/2008 03:43 PM bartjoosen said the following:
Maybe you should provide a minimal, working code with data, so that we all
can give it a try.
In the mean time: take a look at the Rprof function to see where your code
can be improved.
Good luck
Bart
Daniel Folkinshteyn-2 wrote:
Hi everyone!
I have a question about data processing efficiency.
My data are as follows: I have a data set on quarterly institutional
ownership of equities; some of them have had recent IPOs, some have not
(I have a binary flag set). The total dataset size is 700k+ rows.
My goal is this: For every quarter since issue for each IPO, I need to
find a "matched" firm in the same industry, and close in market cap. So,
e.g., for firm X, which had an IPO, i need to find a matched non-issuing
firm in quarter 1 since IPO, then a (possibly different) non-issuing
firm in quarter 2 since IPO, etc. Repeat for each issuing firm (there
are about 8300 of these).
Thus it seems to me that I need to be doing a lot of data selection and
subsetting, and looping (yikes!), but the result appears to be highly
inefficient and takes ages (well, many hours). What I am doing, in
pseudocode, is this:
1. for each quarter of data, getting out all the IPOs and all the
eligible non-issuing firms.
2. for each IPO in a quarter, grab all the non-issuers in the same
industry, sort them by size, and finally grab a matching firm closest in
size (the exact procedure is to grab the closest bigger firm if one
exists, and just the biggest available if all are smaller)
3. assign the matched firm-observation the same "quarters since issue"
as the IPO being matched
4. rbind them all into the "matching" dataset.
The function I currently have is pasted below, for your reference. Is
there any way to make it produce the same result but much faster?
Specifically, I am guessing eliminating some loops would be very good,
but I don't see how, since I need to do some fancy footwork for each IPO
in each quarter to find the matching firm. I'll be doing a few things
similar to this, so it's somewhat important to up the efficiency of
this. Maybe some of you R-fu masters can clue me in? :)
I would appreciate any help, tips, tricks, tweaks, you name it! :)
========== my function below ===========
fcn_create_nonissuing_match_by_quarterssinceissue = function(tfdata,
quarters_since_issue=40) {
result = matrix(nrow=0, ncol=ncol(tfdata)) # rbind for matrix is
cheaper, so typecast the result to matrix
colnames = names(tfdata)
quarterends = sort(unique(tfdata$DATE))
for (aquarter in quarterends) {
tfdata_quarter = tfdata[tfdata$DATE == aquarter, ]
tfdata_quarter_fitting_nonissuers = tfdata_quarter[
(tfdata_quarter$Quarters.Since.Latest.Issue > quarters_since_issue) &
(tfdata_quarter$IPO.Flag == 0), ]
tfdata_quarter_ipoissuers = tfdata_quarter[
tfdata_quarter$IPO.Flag == 1, ]
for (i in 1:nrow(tfdata_quarter_ipoissuers)) {
arow = tfdata_quarter_ipoissuers[i,]
industrypeers = tfdata_quarter_fitting_nonissuers[
tfdata_quarter_fitting_nonissuers$HSICIG == arow$HSICIG, ]
industrypeers = industrypeers[
order(industrypeers$Market.Cap.13f), ]
if ( nrow(industrypeers) > 0 ) {
if ( nrow(industrypeers[industrypeers$Market.Cap.13f >=
arow$Market.Cap.13f, ]) > 0 ) {
bestpeer =
industrypeers[industrypeers$Market.Cap.13f >= arow$Market.Cap.13f, ][1,]
}
else {
bestpeer = industrypeers[nrow(industrypeers),]
}
bestpeer$Quarters.Since.IPO.Issue =
arow$Quarters.Since.IPO.Issue
#tfdata_quarter$Match.Dummy.By.Quarter[tfdata_quarter$PERMNO ==
bestpeer$PERMNO] = 1
result = rbind(result, as.matrix(bestpeer))
}
}
#result = rbind(result, tfdata_quarter)
print (aquarter)
}
result = as.data.frame(result)
names(result) = colnames
return(result)
}
========= end of my function =============
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and provide commented, minimal, self-contained, reproducible code.
